AtCIPK16 Mediates Salt Stress Potentially Through ... › content › 10.1101 › 2020.02.17.953216v2.ful… · AtCIPK16 Mediates Salt Stress Potentially Through Phytohormones and

1

AtCIPK16 Mediates Salt Stress Potentially Through Phytohormones and

Transcription Factors

Running title: AtCIPK16 mediated salt tolerance

Shanika L. Amarasinghe1,2, Wenmian Huang1,2, Nathan S. Watson-Haigh1,2,3, Matthew Gilliham2,4, Stuart

J. Roy1,2,3, Ute Baumann1,2,3

1. Australian Centre for Plant Functional Genomics, PMB 1, Glen Osmond, SA 5064, Australia

2. School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, PMB 1

Glen Osmond, SA 5064, Australia.

3. ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, School of

Agriculture, Food and Wine, The University of Adelaide, Glen Osmond SA 5064, Australia.

4. Australian Research Council Centre of Excellence in Plant Energy Biology, Department of Plant

Science, School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond SA 5064,

Australia.

Correspondence:

Dr. Ute Baumann,

Plant Genomics Centre,

School of Agriculture, Food and Wine,

The University of Adelaide,

Urrbrae,

South Australia,

5064, AUSTRALIA.

.CC-BY-ND 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for thisthis version posted February 20, 2020. . https://doi.org/10.1101/2020.02.17.953216doi: bioRxiv preprint

https://doi.org/10.1101/2020.02.17.953216

http://creativecommons.org/licenses/by-nd/4.0/

2

Abstract

Soil salinity causes large productivity losses for agriculture worldwide. “Next-generation crops” that can

tolerate salt stress are required for the sustainability of global food production. Previous research in

Arabidopsis thaliana aimed at uncovering novel factors underpinning improved plant salinity tolerance

identified the protein kinase AtCIPK16. Overexpression of AtCIPK16 enhanced shoot Na+ exclusion and

increased biomass in both Arabidopsis and barley. Here, a comparative transcriptomic study on

Arabidopsis lines expressing AtCIPK16 was conducted in the presence and absence of salt stress, using

an RNA-Seq approach, complemented by AtCIPK16 interaction and localisation studies. We are now

able to provide evidence for AtCIPK16 activity in the nucleus. Moreover, the results manifest the

involvement of a transcription factor, AtTZF1, phytohormones and the ability to quickly reach homeostasis

as components important for improving salinity tolerance in transgenics overexpressing AtCIPK16.

Furthermore, we suggest the possibility of both biotic and abiotic tolerance through AtCIPK16, and

propose a model for the salt tolerance pathway elicited through AtCIPK16.

Keywords

differential gene expression, gene co-expression, transgenics, cross-tolerance, abiotic and biotic stress

crosstalk


https://doi.org/10.1101/2020.02.17.953216


3

1 Introduction

Soil salinity has adverse effects on global agricultural production (FAOSTAT 2014; Rengasamy

2010; Rengasamy 2006). An estimated 30% of the irrigated land and 6% of the world’s total land

is affected by salt, and these areas are increasing in size (Schroeder et al. 2013). Estimates put

agricultural production losses at 12 billion USD per annum in the US alone (Munns and Gilliham

2015; Shabala 2013). Finding crops that can withstand high salinity therefore is a high-priority for

achieving sustainable world food production. Salinity imposes two main limitations on plant growth

and survival: (i) an initial osmotic stress; and, (ii) secondary nutritional imbalance resulting in ionic

and oxidative stress via accumulation of high concentrations of sodium (Na+) and chloride (Cl–)

(Asif et al. 2019; Munns and Tester 2008; Roy et al. 2014; Shabala et al. 2015; Wungrampha et al.

2018). There is extensive research efforts focused on understanding the molecular mechanisms

that confer increased salt tolerance with the ultimate goal of developing more salt tolerant crops

(Deinlein et al. 2014; Hanin et al. 2016; Munns and Gilliham 2015; Roy et al. 2014).

Molecular mechanisms involved in salt tolerance in plants, can be broadly classified into the

following categories: a) transporters that can reduce influx, or increase efflux or

compartmentalization of Na+/Cl- ions, or maintain K+ homeostasis (e.g. Salt Overly Sensitive 1

(SOS1), N+/H+ exchanger 1 (NHX1), High-affinity Potassium Transporter (HKT) 1;5, HKT1;4,

HKT2;1, Potassium channel 1 (AKT1), Potassium transporter 5 (HAK5), Potassium channel KAT1,

Cation-chloride cotransporter 1 (CCC1), S-type anion channel (SLAH1)) (Bassil et al. 2012; Chen

et al. 2007; Diédhiou and Golldack 2006; Grabov 2007; Hamamoto et al. 2015; James et al. 2011;

Ji et al. 2013; Mian et al. 2011; Qiu et al. 2016; Schachtman et al. 1992; Wang et al. 2015); b)

detoxifiers that can scavenge excessive reactive oxygen species (ROS) and alleviate negative

effects of ROS (e.g. Superoxide dismutases; SODs, ascorbate peroxidase; APX, ascorbic acid;

AsA, catalases; CATs, GSH/glutathione peroxidase; GPX, NADH:peroxiredoxin oxidoreductase;

PrxR) (Baxter et al. 2014; Mittler et al. 2011); c) osmotic adjusters that can maintain low intracellular

osmotic potential in plants under salt stress (for example proline, glycine betaine, free amino acids,

sugars, polyamines and polyphenols) (Rosa et al. 2009); d) phytohormones that can facilitate a

broad array of adaptive responses and long distance signalling (such as abscisic acid, indole acetic

acid, cytokinins, gibberellic acid, salicylic acid, brassinosteroids, jasmonates, ethylene) (Fahad et

al. 2015; Peleg and Blumwald 2011; Ryu and Cho 2015); and e) salt sensors including those that

sense cytosolic Ca2+ changes resulting from changes in the cytosol due to salinity and

communicate the effects to downstream activating proteins (calcineurin-B-like proteins; CBLs and

CBL-interacting protein kinases; CIPKs, calcium-dependent protein kinases; CDPKs, calmodulins;

CaMs, calmodulin-like proteins; CAMLs, etc.) (Shabala et al. 2015).


https://doi.org/10.1101/2020.02.17.953216


4

The involvement of CBL-CIPK complexes as signalling components in salt stress has been well

established (Batistič and Kudla 2009; Hashimoto et al. 2012; Luan 2009; Mao et al. 2016; Thoday-

Kennedy et al. 2015). Arabidopsis CIPKs found to be involved in salinity tolerance mechanisms of

plants include CIPK1 (D’Angelo et al. 2006), CIPK3 (Kim 2003), CIPK6 (Tripathi et al. 2009),

CIPK16 (Roy et al. 2013) and CIPK24 (SOS2) (Liu et al. 2000). AtCIPK16 from Arabidopsis thaliana

(At2g25090) was identified from a forward genetic screen as a gene with a role in reducing Na+

content in leaves during salt stress (Roy et al. 2013). Therefore, AtCIPK16 is a potential candidate

for the genetic engineering of salinity tolerant crops. The knowledge on the mode of action of

AtCIPK16 is still largely unknown; however, previous studies have shown that AtCIPK16 has a

nuclear localisation signal (NLS) (Amarasinghe et al. 2016).

The current study is an attempt to fill a gap in our understanding of AtCIPK16 mediated salt stress

tolerance in A. thaliana. Through an investigation of the transcriptomic responses in transgenic and

null-transgenic plants, as well as a co-expression network analysis, we aimed to identify a set of

genes, whose expression is influenced directly or indirectly by AtCIPK16 overexpression. Our

results suggest that the AtCIPK16 mediated salt tolerance is mainly achieved through transcription

factor modulation and phytohormone signalling. We propose a molecular pathway for at least a

part of the AtCIPK16 mediated salt tolerance mechanism for validation in future laboratory

experiments.

2 Materials and Methods

2.1 Yeast two hybrid-assay

Full-length of the coding sequences of all 10 AtCBLs and AtCIPK16 were amplified from cDNA of

Arabidopsis ecotype Columbia-0 by high fidelity PCR using primers listed in Supp Table 1 and

cloned into a pCR8/GW/TOPO TA Gateway® entry vector (Invitrogen, USA) following the

manufactures protocols. An LR reaction was performed to transfer the DNA from pCR8 into the

required destination vectors, the prey vector pTOOL28 and the bait vector pTOOL27. The

AtCIPK16 bait vector, 10 AtCBLs prey vectors were co-transformed into the yeast strain AH109

following manufacturer's instructions (Clontech, CA, USA). The empty vectors pTOOL27 and

pTOOL28 were also co-transformed into yeast with each prey or bait plasmid respectively to

confirm that bait does not autonomously activate the reporter genes in the absence of the prey

protein.

For each possible combination between bait and preys the largest colonies were picked from each

plate and inoculated into 5 mL selection SD QDO liquid medium (containing 2 % glucose), followed


https://doi.org/10.1101/2020.02.17.953216


5

by incubation at 30 °C with shaking at 180 rpm (orbital mixer incubator, Ratek Instruments,

Australia) for 2 days. OD600 of yeast liquid samples were determined by an ND-1000

spectrophotometer (NanoDrop Technologies Inc.) and adjusted on OD600 of 0.25 with sterile Milli

Q water. To establish whether the bait and prey proteins interacted on the plates, 10 μL of 10-fold

serial dilutions of yeast harbouring an activation domain (AD)-AtCBLs or empty vector pTOOL28

and either DNA-binding domain (BD)-AtCIPK16 or empty vector pTOOL27 were spotted on plates

containing DDO (SD/-Leu/-Trp, as a control) and QDO (SD/-Leu/-Trp/-His/-Ade (Clonetech) to

determine whether there is an interaction between prey and bait molecules. Both DDO and QDO

selection plates were incubated at 30 °C for 5-8 days to prior to the growth of yeast being observed.

2.2 Bimolecular fluorescence complementation (BiFC) assay using both transient

expression and stable expression

To confirm the interaction of AtCIPK16 with 10 AtCBL proteins and to localise the interaction

complex of AtCBL-AtCIPK16 in Planta, a bimolecular fluorescent complementation (BiFC) assay

was performed using transient expression in tobacco leaf epidermal cells.

The full-length coding sequence of 10 AtCBLs and AtCIPK16 in pCR8 with or without the stop

codon were cloned into a pCR8/GW/TOPO TA Gateway® entry vector (Invitrogen, USA). An LR

reaction was performed to transfer genes into a pGPTVII (need ref) destination vector.

Consequently, each AtCBL was expressed as a fusion to C-terminal YFP fragment (YC) and

AtCIPK16 was expressed as a fusion to N-terminal YFP fragment (YN). Constitutive expression of

the genes of interest in plants is ensured by the 35S promoter of the cauliflowers mosaic virus in

the vectors.

Pairs of 10 AtCBLs and AtCIPK16 in pGPTVII constructs were co-transformed into A.tumefaciens

AGL1 cells using the protocol adapted from Kapila et al.(1997) and Wydro et al. (2006). Mixtures

containing pairs of AtCBLs-AtCIPK16 were aspirated into a 1 mL needless syringe (Terumo, Tokyo,

Japan) and injected into the abaxial side of the third and fourth leaves of different six-week-old

tobacco until saturated. After infiltration, the transformed tobacco plants were then kept in short day

growth room (light/dark period: 10h/14h, temperature: 23 °C, light intensity: 100 μmol m-2 s-1, 60-

75 % humidity) for 3-4 days to allow the expression of AtCBLs-AtCIPK16 before harvesting leaf

material for visualisation by confocal.

Location (if any) of the interaction of fluorescently tagged AtCIPK16 and the different AtCBLs in

leaf cells was determined using a A Zeiss Axioskop 2 MOT plus LSM5 PASCAL confocal laser

scanning microscopy equipped with an argon laser (Carl Zeiss, Jena, Germany).: YFP signal was

detected using an excitation of 514 nm and an emission bandpass of 570-590 nm. Propidium


https://doi.org/10.1101/2020.02.17.953216


6

iodide/chlorophyll autofluorescence was detected using an excitation of 543 nm and an emission

longpass filter of 560 nm. False colour images were analysed using LSM5 Image Examiner (Carl

Zeiss, Jena, Germany).

2.3 RNA-Seq Assay

2.3.1 Experimental Design

The RNA-Seq study has a 2 (genotype: null, transgenic) by 2 (tissue: root, shoot) by 2 (treatment:

control, salt treated) by 2 (time: 3 hr, 51 hr) factorial design. To ensure a minimum number of 4

biological replicates for the RNASeq analysis six A. thaliana replicate plants were sampled for each

experimental group of which 4 were sent for sequencing. The final experimental design is

summarised in Table 1. For the 3 hour time point there were two technical replicates per biological

sample hence represented as a multiple of 2.

2.3.2 Transformation of AtCIPK16, T2 seed germination, Plant material, growth

conditions, salt treatment and sampling

Transgenic 35S:AtCIPK16 overexpressing Arabidopsis thaliana, Col-0, were previously generated

as described in Roy et al. 2013. The plant growth in hydroponics was conducted according to Jha

et al. (2010). Seeds of T2 35S:AtCIPK16 plants were soaked in 70% ethanol for 2 minutes followed

by 4-5 rinses in sterile milli-Q water for surface sterilisation. Subsequently the seeds were planted

in 1.5 mL microfuge tubes containing half-strength Arabidopsis nutrient solution (Arteca and Arteca

2000) and 0.8% Bacto agar. After vernalisation for 2 days at 4°C, seeds were transferred to a

growth room with controlled light (10 hr light/14 hr photoperiod, an irradiance of 70 mmol m-2s-1)

and constant temperature of 21 °C. After emergence of the seedling roots, plants were transferred

to a hydroponic tank containing full-strength Arabidopsis nutrient solution (Arteca and Arteca 2000).

The pH of the hydroponic solution was maintained at 5.7. After 5 weeks of growth in hydroponics,

salt stress was applied to half the plants by the addition of 75 mM NaCl. Calcium activity in the

growth medium was maintained at 0.3 mM by the addition of the correct amount of CaCl2, as

calculated using Visual Minteq Version 2.3 (KTH, Department of Land and Water Resources

Engineering, Stockholm, Sweden). Shoot and root tissues were removed after 3 hours and 51

hours of salt stress for RNA extraction and were immediately frozen in liquid nitrogen. To avoid

effects on gene expression due to the circadian rhythm the second sampling was conducted at 51

hours rather than 48 hours (2 days) after stress treatment RNA isolation, library preparation and

Illumina sequencing. Prescence of the transgene in the AtCIPK16 expressing lines was determined

as before (Roy et al., 2013).


https://doi.org/10.1101/2020.02.17.953216


7

Total RNA was extracted using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA), following the

protocol described by Chomczynski (1993). TruSeq™ stranded RNA sample preparation was

utilized with dUTP second strand marking protocol for cDNA library preparation. Ribo-Zero kit

(Epicentre, an Illumina company, Madison, WI) was used to remove rRNA from the libraries.

Illumina Sequencing was carried out to collect 100bp paired-end (2 * 100bp). Aim was to get a read

depth of 15 Mill read pairs per library.

2.3.3 RNA-Seq data pre-processing

Raw data was examined by the program FASTQC for read quality, detection of adapter

contaminations and presence of overrepresented sequences (Andrews 2010). Next, a Java based

in-house k-mer counting algorithm was used to confirm the presence/absence of the transgene in

each sample by counting reads belonging to the UTRs of the transgene and the wild type

respectively. As described in Roy et al. (2013), the CIPK16 transgene was inserted inbetween the

35S CaMV promoter and 3′UTR of the nos terminator. To distinguish expression of the transgene

from the endogenous AtCIPK16 in Col-0 plants, an k-mer counting script available in LNISKS

package (https://github.com/rsuchecki/LNISKS; Suchecki et al., 2019) was supplied with these

sequences ( AtCIPK16 5′ UTR, AtCIPK16 3′ UTR and region in between the AtCIPK16 exon 1 and

the AtCIPK16 5′ UTR) to count reads belonging to the expressed transgene from the FASTQ files.

Furthermore, regions 20 kb upstream and downstream of the AtCIPK16 gene obtained from the

TAIR database (https://www.arabidopsis.org/) were used to count the reads expressed from the

endogenous wild-type AtCIPK16.

Reads with lengths ≥ 70 bp after quality trimming were used for further processing. The Arabidopsis

reference genome TAIR10, and gene model annotation files were downloaded from the TAIR ftp

site (http://www.tair.org). Read alignment to the reference genome was performed using the splice

aligner STAR (version 2.4.1c) (Dobin et al. 2013). There are two steps in mapping using the STAR

aligner. 1; Building a genome index for the reference genome (FASTA sequences): A. thaliana GFF

file was used with an overhang of 100 (i.e. max readLength -1) for creating the index. The

chloroplast and mitochondrial genomes were excluded when creating these index files. 2; Mapping

the reads to the genome: the paired-end reads were mapped to the reference with no mismatches

allowed (because both reference and samples were from Col-0 cultivar), with a maximum intron

size of 2000 and a maximum gap of 2000 between two mates. Alignment results were output in

Sequence/Binary Alignment Map (SAM/BAM) format sorted by the chromosome coordinates.

Alignments with non-canonical junctions were filtered out. Quantification of gene expression level

and identification of Differentially Expressed Genes (DEGs)


https://github.com/rsuchecki/LNISKS

http://www.tair.org/

https://doi.org/10.1101/2020.02.17.953216


8

Read counting for the transcripts was done using the featureCounts() function of the Rsubread

package (Liao et al. 2014) implemented in the R environment (http://www.R-project.org). DGEList()

function from DESeq R library was used to calculate the counts per million (CPM) for each

experimental group based on the count matrix, and the calcNormFactors() function was applied to

estimate normalization factors (Anders and Huber 2010). Data from each time point (i.e. 3 hr, 51

hr) and each tissue were analysed separately resulting in 4 groups (i.e. Root_3Hr, Shoot_3Hr,

Root_51Hr and Shoot_51Hr). Transcripts with CPM values of less than 100 in 75% of the samples

or more were filtered out. Out of 64, 60 samples at the 3 hour time point were analysed. A sample

from root and the corresponding shoot sample (of which each had 2 technical replicates; Table 1)

from the 3 hour time point had to be removed due to a large variation in AtCIPK16 expression

compared to other samples (S3).

T-statistics for mean expression values for each gene was determined using the LIMMA (Linear

Models for Microarray Data) package implemented in the R software environment (Ritchie et al.

2015; Smyth 2004). The read count data fed into the Limma linear model fitting were transformed

using Voom with quantile normalization followed by group-means parameterization and robust

eBayes (Law et al. 2014; Smyth 2004). The contrast matrix was created with the final goal of;

identifying the differentially expressed genes in salt stress that is due to the definite effect of the

transgene. This contrast matrix was used on the 4 groups of expression value data separately. P-

values for multiple testing were corrected according to Benjamini and Hochberg 1995. Differentially

expressed genes are those with a FDR-adjusted P value of ≤0.05 and ≥2-fold change in expression

relative to the control.

2.3.4 Regulatory network construction using WGCNA

Weighted Gene Co-expression Network Analysis (WGCNA) enables the detection of modules of

genes with high expression value correlation to one another (Langfelder and Horvath 2008). Briefly,

WGCNA assigns a connection weight between pairs of genes within the network based on a

biologically motivated criterion and attempts to identify relevant modules by applying a soft

threshold to correlations between pairs of genes within a network. R software environment was

used for all WGCNA analysis (Langfelder and Horvath 2008; Zhang and Horvath 2005). After

confirming that there were no outliers in the tissue-separated samples optimization of the soft

threshold values was performed. Signed co-expression network was constructed using the

automatic one-step network construction method (function cuttreeDynamic()) with following

settings; a signed type of network, an unsigned type of topological overlap matrix (TOM),

correlations of the network raised to a soft thresholding power β (roots:10; shoots: 5), correlation

measures with option ‘bicor’, deepSplit value of 2, a minimum module size of 20. The first principle


https://doi.org/10.1101/2020.02.17.953216


9

component of a module (module eigengene) value was calculated and used to test the association

of modules with salt response in the null and transgenic genotypes. Total network connectivity

(kTotal), and module membership (MM), were calculated for each of the DEGs. Modules for further

analysis were selected if one or more of their hub genes (genes with modular membership ≥ 0.9)

was among the transgene dependent salt-responsive gene list of the respective tissue.

2.3.5 Functional analysis

GO term enrichment was performed using the Plant Gene Set Enrichment Analysis Toolkit

(PlantGSEA) and the DAVID online web server (http://david.abcc.ncifcrf.gov/) (Dennis et al. 2003;

Yi et al. 2013). Up and down regulated DEGs from each contrast were used for GO and pathway

enrichment analysis, and a False Discovery Ratio (FDR) corrected p value ≤ 0.05 was selected as

the threshold level of significance to determine the enrichment in the gene set. MapMan stand-

alone software allowed the assignment of DEGs into regulatory pathways (Thimm et al. 2004).

Additionally, Kyoto Encyclopaedia of Genes and Genomes (KEGG) was used to identify higher

order functional information related to the DEGs (Kanehisa et al. 2017). ATTED-II (http://atted.jp)

gene co-expression database was mined to identify additional yet relevant genes that may be co-

expressed with the DEGs (Aoki et al. 2016).

To identify genes that are exclusively differentially expressed only in transgene dependent manner

in presence of salt, the DEGs from the transgene dependent salt responsive gene list was

compared in a pairwise manner to the DEGs from that of transgene effect in controls for a given

tissue at a particular time point.

Phosphorylation targets were identified using the NetPhoS4.1 online server (Blom et al. 1999).

MPK substrates were identified by comparing the DEGs to known substrates recorded in the

literature (Meng et al. 2013; Nguyen et al. 2012; Popescu et al. 2009; Vogel et al. 2012). Promoter

analysis for the ARE motif; regions 3000 bp upstream from the transcription start site (TSS) of all

transgene dependent salt responsive genes of root and shoot at 3 hours were downloaded from

the TAIR database (https://www.arabidopsis.org/). The motif pattern ATTTATTTATTT{A|T] was

searched against the downloaded sequences through the FIMO tool (http://meme-

suite.org/tools/fimo) in the MEME suite (Grant et al. 2011). The p-value threshold was set to 0.01.

DNA binding domains and amino acid properties were identified from protein sequences using the

consensus of results obtained through several freely available online tools with the use of their

default settings; DP-BIND (Hwang et al. 2007), BINDN (Wang and Brown 2006), NetSurfP

(Petersen et al. 2009), PredictProtein (Rost et al. 2004), paircoil2 (McDonnell et al. 2006) and

pepinfo (Li et al. 2015).


http://david.abcc.ncifcrf.gov/home.jsp

https://www.arabidopsis.org/

http://meme-suite.org/tools/fimo


https://doi.org/10.1101/2020.02.17.953216


10

3 Results

3.1 Determining AtCIPK16 interacting CBLs

AtCIPK16 was found to interact with 6 of the 10 AtCBL proteins. AtCIPK16 was found to interact

strongly with AtCBL4 and AtCBL5; moderately with AtCBL2 and AtCBL9; and weakly with AtCBL1

and AtCBL10 (Figure 1). In contrast, AtCIPK16 did not interact with AtCBL3, AtCBL6, AtCBL7 and

AtCBL8 (Figure 1). Similar growth of all yeast samples were observed on the control SD (-Leu/-

Trp) plates (Figure 1).

3.2 Resolving subcellular localization of AtCIPK16:CBL interactions

YFP fluorescence in the nucleus and possibly at the plasma membrane was observed for AtCBL1-

AtCIPK16, AtCBL4-AtCIPK16, AtCBL5-AtCIPK16 and AtCBL9-AtCIPK16 (Figure 2 a, d, e and i).

AtCBL2-AtCIPK16 and AtCBL10-AtCIPK16 complexes were observed in the cytoplasm and

nucleus (Figure 2 b and j), while AtCBL3-AtCIPK16 was observed in the cytoplasm (Figure 2c).

However, no clear fluorescent signal was observed for AtCBL6-AtCIPK16, AtCBL7-AtCIPK16 and

AtCBL8-AtCIPK16 complexes (Figure 2 f, g and h). Based on this observation, AtCIPK16 sequence

was tested for DNA binding potential. It was predicted that the protein region from A357 – G391 has

the ability to bind to DNA (Figure 3).

3.3 Determining presence and transgene expression level in 35S:AtCIPK16 expressing

Arabidopsis

Presence of the transgene was determined by using primers designed to the transgene specific

3′ UTR region of the gene and confirmed only transgenic plants contained the AtCIPK16 transgene

(S1). AtCIPK16 transgene expression was higher in transgenic plants compared to native

AtCIPK16 expression in null segregants in the absence of salt stress, and after both 3 and 51 hours

of salt stress (Figure 4).

3.4 Differential Gene Expression

To determine differential gene expression between AtCIPK16 over-expressing lines and null

segregants, RNA was extracted from shoot and root material of 5 week old, hydroponically grown,

plants exposed to either 0 or 75 mM NaCl for 3 or 51 hours. RNA-Seq analysis was performed to

determine the plants’ gene expression profiles. On average, a mapping percentage of ~88% was

reported across root and shoot material collected from both transgenic and null segregants for the

3 hour time point data and a mapping percentage of ~86% for 51 hour time point samples (S2).


https://doi.org/10.1101/2020.02.17.953216


11

A total of 21,974 and 21,160 genes were differentially expressed across the two tissues from 3

hours and 51 hours, respectively, in salt treated AtCIPK16 transgenic plants compared to the null

transgenics. In order to identify the differentially expressed genes in salt stressed plants with

AtCIPK16 overexpression several contrasts were tested (Figure 5) based on the differences in

gene expression levels in the roots at 3 hours, shoots at 3 hours, roots at 51 hours and shoots at

51 hours. The number of genes which were up or down-regulated at each of the two time points

and in each tissue for each line are shown in Figure 6.

3.4.1 Contrast 1: Transgenic Control Vs Null Control

In the comparison of transgenic control vs null control (transgene-effect in controls; TC) samples,

5 differentially expressed genes (DEGTC) in the roots at 3 hrs (DEGTC(3R)) and 160 in the shoots at

3 hrs (DEGTC(3S)) were identified (Figure 6a, S4 worksheet 1-2). As expected, AtCIPK16

(AT2G25090) was present in both DEGTC(3R) and DEGTC(3S) (Figure 7a). Most (150) of the genes in

DEGTC(3S) had higher expression in transgenics.

At 51 hours, there was only 1 DEGTC in roots (i.e. DEGTC(51R)) (Figure 6a; S4 worksheet 3). In shoot

controls at 51 hours there were 17 DEGTC (i.e. DEGTC(51S)) (Figure 6a; S4 worksheet 4). While

there is 1 DEGs in common between the root and the shoot DEGTC at 51 hours, it is not AtCIPK16

but AT1G47970 (Figure 7b).

3.4.2 Contrast 2: Transgenic Salt Vs Null Salt

This is the comparison of transgenic vs null samples in presence of salt (transgene effect in salt:

TS) (Figure 5) There were DEGs (DEGTS) present for this contrast for both tissues at both time

points (i.e. DEGTS(3R): 403, DEGTS(3S): 108, DEGTS(51R): 4, DEGTS(51S): 13) (Figure 6b; S4 worksheet

5, 6, 7 and 8). While there was a ~80 fold increase in the DEGTS(3R) compared to DEGTC(3R) (403 vs

5), the DEGTS(3S) remained more or less in the same range as DEGTc(3S) (160 vs 108) (Figure 6 a

and b). However, there were proportionally more down regulated genes in the DEGTS(3S). While

only ~6% of the DEGs from DEGTC(3S) were down regulated (10/160), ~37% of DEGTS(3S) were

down regulated (40/108). Clearly during salt stress, overexpression of AtCIPK16 reduced the

expression of genes.

At 51 hours, a very low number of DEGs were seen in salt stress in both roots and shoots similar

to the observations in non-stressed conditions (4 DEGTS(51R) vs 1 DEGTC(51R) and 13 DEGTS(51S) vs

17 DEGTC(51S)) (Figure 6 a and b). There were 10 genes in common between DEGTS of root and

shoots at 3 hours (Figure 7c) while there were none at 51 hours (Figure 7d).


https://doi.org/10.1101/2020.02.17.953216


12

3.4.3 Contrast 3: Transgenic Salt Vs Transgenic Control and Contrast 4: Null Salt Vs

Null Control

At 3 hours, in both tissues the effect of salt (salt effect on transgenics: ST; Figure 5) elicited

differential expression of more genes (DEGST) in AtCIPK16 transgenics (i.e. DEGST(3R): 1696 and

DEGST(3S): 572) compared to the null transgenics (salt effect on nulls: SN; Figure 5) (DEGSN(3R): 849

and DEGSN(3S): 439) (Figure 6 c and d). But at 51 hours it is the opposite; effect of salt elicited the

differential expression of fewer genes in AtCIPK16 transgenics (i.e. DEGST(51R): 123 and DEGST(51S):

135) compared to nulls (DEGSN(51R): 1043 and DEGSN(51R): 358) (Figure 6 c and d).

3.4.4 Contrast 5: Interaction between SN and ST

With the presence of DEGs in both ST and SN, genes with significantly different expression levels

between these two contrasts were examined through linear modelling of data. The results of

contrast 5 are differentially expressed genes due to the absolute effect of transgene in salt stress

(INT) (i.e. DEGINT) (Figure 5). Even though for 3 hours there were 231 DEGINT in roots (DEGINT(3R))

and 152 DEGINT in shoots (DEGINT(3S)), there were no DEGINT for the 51 hours (Figure 6e; S4

worksheet 9, 10, 11 and 12). Furthermore, there were 9 DEGINT common to both roots and shoots

at 3 hour time point (Figure 7e).

3.5 Investigating potential biological implications of AtCIPK16 overexpression

Genes with a significant transgene-effect in controls (5 DEGTC(3R), 160 DEGTC(3S), 1 DEGTC(51R) and

17 DEGTC(51S)) (Figure 6a) were further analysed through Gene Ontology (GO) studies and pathway

analysis to understand potential biological consequences of AtCIPK16 overexpression in the non-

stressed conditions. GO analysis showed that DEGTC(3S) that are up-regulated were most enriched

for response to chitin (p value = 3.47×10-92) (S5; worksheet1 cells with yellow background colour).

Perhaps not surprisingly, the corresponding Kyoto Encyclopaedia of Genes and Genomes (KEGG)

pathways that DEGTC(3S) fell into include plant-pathogen interaction (S6, column B). Additionally,

molecular functions such as transcription regulator activity was significant for the DEGTC(3S) that are

up-regulated (p value = 7.51×10-09) (S5; worksheet 1 cells with yellow background colour). Down-

regulated DEGTC(3S) were enriched for the molecular function of negative regulation of RAS protein

signal transduction (p value = 6.95×10-04) and RHO-GTPase binding (p value = 6.95×10-04)

(S5, worksheet1 cells with blue background colour).

The significant GO terms found for up-regulated DEGTS(3R) were the biological process response

to organic substance (p value = 1.59×10-42) and the molecular function sequence specific DNA

binding transcription factor activity (p value = 5.65×10-11) (S5, worksheet 2 cells with yellow

background colour). The up-regulated DEGTS(3S) were enriched for cell wall modification involved


https://doi.org/10.1101/2020.02.17.953216


13

in abscission (p value = 7.28×10-04) and indole-3-acetic acid amido synthetase activity

(p value = 1.52×10-03) (S5, worksheet 3 cells with yellow background colour). The down-regulated

DEGTS(3S) were enriched for terms such as cellular response to iron starvation

(p value = 7.03×10- 07) and iron ion binding (p value = 6.37×10-04) (S5, worksheet3 cells with blue

background colour). The molecular functions related to metal binding, interestingly had a focus on

calcium ion binding in salt absent shoots (S5, worksheet1 cells with yellow background colour)

while it is more DNA and Ferric ion binding for salt stressed roots (S5, worksheet2 cells with yellow

background colour) and shoots (S5, worksheet3 cells with yellow background colour), respectively.

The transgene dependent salt responsiveness was investigated for the combined effect of both

AtCIPK16 overexpression and salt on the plant. DEGINT(3R) were enriched for response to chitin

(p value = 3.08×10-26) (S5, worksheet 4 biological process). The pathways the DEGINT(3R) fall in

included “carbon metabolism”, “Phenylpropanoid biosynthesis”, “Glyoxylate and dicarboxylate

metabolism” and “Galactose metabolism” (S6 column E). Response to carbohydrate stimulus

(p value = 5.13×10-10) was a GO category identified for the DEGINT(3S) (S5, worksheet 5 biological

process). Peroxidases that are involved in the Phenylpropanoid biosynthesis pathway and genes

involved in flavonoid biosynthesis were identified through pathway analysis (S6 column F).

Furthermore, Calcium-binding EF-hand motif containing genes involved in plant-pathogen

interaction were among the pathways which DEGINT(3S) were grouped into (S6, column F).

Next specific roles of DEGs were investigated in the following functional categories; a)

transporters/channels, b) regulation of transcription, c) metal handling, d) enzyme families, e)

hormone metabolism and f) signalling pathways (S7).

3.5.1 Transporters/Channels

More transporters were identified as DEGTS(3R) compared to transporter DEGTS(3S) and DEGTC(3S)

(S7 worksheet 1, under Transport in S8 a, b, d and e). The transporter genes from the DEGTC(3S)

included SLAH3. The transporter DEGTS(3R) included several NRTs, CHX17, CNGC19, root hair

specific 2 genes. Furthermore, there are transporters from DEGTS(3R) associated with JAZ proteins

that are involved in ubiquitination leading to proteolysis, as well as SAUR protein coding genes

involved in cell expansion through auxins (S9 a). Other pathways that DEGTS(3R) belonged to were:

Phenylpropanoid biosynthesis and phenylalanine metabolism (S10 a) and “valine, leucine and

isoleucine degradation related genes”. While transporter DEGTS(3S) are directly or indirectly

associated with cell wall biosynthesis, α-Linolenic acid metabolism and Pentose and glucuronate

interconversions were associated with DEGINT(3S) (S10 a).


https://doi.org/10.1101/2020.02.17.953216


14

3.5.2 Regulation of Transcription and DNA/DNA Processing

The largest number of DEGs encoding transcription factors (TFs) belonged to DEGTS(3R)

(S7 worksheet 2, under regulation of transcription in S8 d). Only five of these TFs were identified

in pathways and they fell into plant hormone transduction and plant-pathogen interaction pathways

(S10 b). The TFs from DEGTS(3S) were related to limonene and pinene degradation, ubiquitin

mediated proteolysis, starch and sucrose metabolism and stilbenoid, diarylheptanoid and gingerol

biosynthesis (S10 b). The TF genes from DEGINT(3R) and DEGINT(3S) are directly or indirectly involved

in plant-pathogen interactions, starch and sucrose metabolism and plant hormone transduction

(S10 b). The hormone signal transduction related genes from DEGINT(3R) are related to auxin, ABA

and jasmonic acid (S9 b). RNA synthesis and processing genes were not in DEGINT(3R) while they

were in DEGTS(3R) (under RNA synthesis and RNA processing in S8 d and h). DEGTC(51S) contained

DNA replication and nucleotide excision repair pathway genes, which included the transcription

factor NF-YB11 (AT2G27470) (S6 column J).

3.5.3 Metal Synthesis and Assimilation

There are metal related genes within both DEGTS(3R) and DEGTS(3S) which are not in DEGTC(3R) or

DEGTC(3S) (under metal handling in S8 a, d and e). Moreover, DEGINT at 3 hours contain metal

handling genes that are iron (Fe) related (S7 worksheet 3). Pathways these metal binding DEGINT

directly or indirectly modulate include Porphyrin and chlorophyll metabolism (S10 c).

3.5.4 Enzyme Families

There were ‘enzyme related’ 1 DEGTC(3R) and 2 DEGTC(3S) (S7 worksheet 4, under enzyme families

in S8 a). However, there are at least 12 ‘enzyme related’ DEGTS (S7 worksheet 4, under ‘enzyme

families’ in S8 d and e). Enzyme related DEGINT(3R) were fewer compared to those from DEGTS(3R)

but the number of enzyme related genes from DEGINT(3S) and DEGTS(3S) were more or less similar

(S7 worksheet 4). ‘Enzyme family’ DEGTS showed associations to genes that fell into pathways of

ROS mediation, pathogen interactions and cell growth, and cell wall strengthening (S10 d).

Phenylalanine metabolism and phenylpropanoid biosynthesis were seen to be pathways the

differentially expressed enzymes of 3 hour DEGINT grouped into (S10 d).

3.5.5 Hormone Metabolism

The hormone related DEGTC(3S) were directly or indirectly involved in ethylene, auxin and

brassinosteroid metabolism (S8 j, S9 c). Additionally, 66 of the DEGs from this contrast have

putative involvement in biotic stress which include the ethylene signalling related genes and

ethylene-responsive element binding protein family genes (S8 q).


https://doi.org/10.1101/2020.02.17.953216


15

Within DEGTS(3R) there were genes that were either directly associated to or indirectly modulating

genes related to gibberellin, ethylene, auxin, brassinosteroids and JA (S8 l, S9 d). Several genes

encode products that are known to be involved in ethylene biosynthesis (1-aminocyclopropane-1-

carboxylic acid (acc) synthase 6; AT4G11280) and JA biosynthesis (allene oxide cyclase

2; AT3G25770, allene oxide synthase; AT5G42650) were evident within DEGTS(3R)

(S7 worksheet 5). The potential function of the proteins encoded by these genes mainly was

ubiquitination mediated proteolysis (S9 d). However, it was observed that there are genes related

to auxin metabolism that may also be related to ubiquitination related proteolysis or plant growth

from DEGTS(3S) and DEGINT(3R) (S8 m, o and p; S9 e and f). Plant pathogen interactions were

suggestive as a function of the proteins encoded by DEGTC(3S) and DEGTS(3R) (S10 f). A gene of

which the product is regulated by ethylene and JA (CEJ1; AT3G50260) was differentially expressed

as a DEGINT(3S) (S7 worksheet 5). Furthermore, the only hormone related gene that was

differentially expressed in shoots DEGTS(51S) was GASA14 (AT5G14920) (S7 worksheet 5, S8 n).

3.5.6 Putative biotic stress related signaling pathways

Compared to the number of signalling related genes in putative biotic stress pathways from

DEGTC(3S), there were fewer numbers in DEGTS (S7 worksheet 6, under signalling in S8 q, r and s).

Calcium signalling genes dominated the biotic stress related signalling pathway DEGTC(3S)

(S7 worksheet 6). Additionally to the groups of genes in putative biotic pathways that were a

DEGTS(3S), DEGTS(3R) had genes related to ROS mediation, signal recognition and propagation to

the MAPK cascade and heat shock (S8 q, r and s). While DEGTS(3S) were involved in starch and

sucrose metabolism, DEGINT contained genes in phenylpropanoid biosynthesis in both roots and

shoot (S10 g).

3.6 Narrowing Down on Potential Genes Involved in the AtCIPK16 Dependent Salt

Response

A pairwise comparison of DEGINT(3R) with the DEGTC(3R) revealed that there are 187 genes out of

the 231 genes that are only expressed in a transgene dependent manner in salt (S11 worksheet

1). Furthermore, out of the 152 DEGINT(3S), 120 are uniquely expressed as a transgene dependent

salt response, compared to the transgene effect on non-stressed plants (S11 worksheet 2).

The GO terms such as response to ethylene activated signalling pathway, response to wounding

and response to chitin were enriched for this subset of 187 genes from root at 3 hours (S11

worksheet 3). Functional clustering of these 187 genes in DAVID revealed the presence of 24

transcription factors, 10 ethylene responsive genes and 15 iron related genes (S11 worksheet 4).


https://doi.org/10.1101/2020.02.17.953216


16

The subset of 120 genes from shoot 3 hours were enriched for GO terms such as cellular response

to iron starvation, response to chitin and iron ion homeostasis (S11 worksheet 3). The functional

clustering in DAVID revealed that the 120 subset contains genes involved in ‘nucleus’, ‘metal

binding’ and ‘transcription regulation’ (S11 worksheet 4).

3.7 Narrowing Down on Transcription Factors Putatively Controlled by AtCIPK16

AtCIPK16 was thought to be directly phosphorylating one or more transcription factors in the

presence of salinity. To investigate if this was the case, transcription factors with a significant

transgene effect only in salt responsiveness were identified; the TFs from DEGINT were compared

to TFs from DEGTC. Any genes that were common to these two sets were thought to be differentially

expressed due to the transgene, yet not explicitly due to transgene effect in salinity. On the other

hand, TF genes that were exclusively DEGINT from both roots and shoots at 3 hours were

considered as explicitly expressed due to transgene n presence of salt. There were 25 and 16 TFs

that were thus, exclusive to DEGINT(3R) and DEGINT(3S), respectively (S7 worksheet 2; yellow

background). Interestingly, there was only one such exclusive TF gene common to DEGINT(3R) and

DEGINT(3S) (AT2G25900; AtTZF1) (S7 worksheet 2; yellow background, bold with black border).

It was previously shown that AtTZF1 acts as a transcription factor and binds ARE promoter domains

in AU rich regions Pomeranz (Pomeranz et al., 2011; Qu et al., 2014). Therefore, in order to identify

potential downstream transcriptional regulatory targets of AtTZF1 in AtCIPK16 overexpression

lines, the region 3000 bp upstream of the transcription start site of all root and shoot transgene

dependent salt responsive genes was scanned for the ARE motif through the FIMO tool in MEME

suite. In roots 14 such genes with 17 putative ARE promoter motifs were discovered (Table 2). In

shoots 10 genes with 13 putative promoter ARE motifs were identified (Table 2).

3.8 Known DEGs with Potential Phosphorylation Ability with a Focus on MAPK

Phosphorylated DEGs

Furthermore, the NetPhoS4.1 phosphorylation prediction server results showed that the above

subset of 187 genes from DEGINT(3R) contained 181 genes that code for amino acid sequences

containing multiple serine/threonine phosphorylation sites (S11 worksheet 5). Furthermore,

NetPhoS4.1 server shows that, 109 out of 120 DEGINT(3S) could potentially be phosphorylated with

a given score ≥ 0.9 (S11 worksheet 6).This observation was not surprising because the consensus

sequence of a phosphorylation site is less than 20 amino acids long.

The ability of protein phosphorylation is best studied for the MAPK cascade in various stress

conditions. Therefore, genes that are phosphorylated by various MPKs were identified. There were


https://doi.org/10.1101/2020.02.17.953216


17

twelve and two DEGINT(3R) and DEGINT(3S), respectively, that are potential targets of the MAPK

phosphorylation (S12). Majority of the identified substrates are phosphorylated by MPK6 (S12).

While the nine DEGs that are common between DEGINT(3R) and DEGINT(3S) are showing the ability

to get phosphorylated (S11 worksheet 5 and 6), ZAT10 (AT1G27730) and ATCTH (AT2G25900)

are also known to be substrates of the MAPK cascade (S12).

3.9 Co-expression Analysis

3.9.1 Roots

The WGCNA network analysis created 66 modules. Hub genes of a module are comprehended as

the key drivers of that module which have highest connectivity to the module (i.e. most responsible

for the intact network topology). In order to identify the effect of transgene dependent salt

responsiveness on these modules (i.e. gene clusters), hub genes from each cluster were screened

for DEGINT(3R). Out of the 86, 14 modules contained one or more DEGINT(3R) as hub genes

(S13 worksheet 1) and were selected for further investigations. The genes in each selected module

are in S13 (worksheet 2). Hub genes from the modules are shown in S13 (worksheet 3) and the

DEGINT(3R) are highlighted in yellow. Since there were no transgene dependent salt responsive

genes in roots, no such analysis was performed for the 51 hour time-point.

To extend the network analysis further and retrieve biological relevance underlying the identified

modules from 3 hours, functional enrichment analysis of genes in the selected 14 modules was

performed (S13 worksheet 4, 5 and 6). The green module that contained 1026 genes was highly

enriched for the biological process (BP) response to chitin (p value = 2.87×10-18) (S13 worksheet 4).

The darkgrey module was enriched for the term ‘response to wounding’ (p value = 3.81×10-05) while

pink module was enriched for ‘defence response’ (p value = 2.75×10-09) (S13 worksheet 4).

Interestingly, the lightsteelblue1 module was highly enriched for photosynthesis

(p value = 1.89×10- 61). The other modules were enriched for the terms ‘response to water

deprivation’, ‘response to abscisic acid’, ‘response to absence of light’, ‘circadian rhythm’,

‘autophagy’, ‘rRNA modification’, ‘cell wall organisation’, ‘response to karrikin’, ‘oxidation reduction

process’ and ‘syncytium formation’ (S13 worksheet 4).

AtCIPK16 was found in the yellow module and co-clustered with AtHKT1 (AT4G10310)

(S13 worksheet 2) and trehalose phosphate synthase 10 (AT1G60140). The yellow module is

enriched for ‘carbohydrate metabolic process’ (p value = 0.002) and ‘sodium ion transport’

(p value = 0.002) (S13 worksheet 4). The KEGG pathways the yellow module genes fall into include

starch and sucrose metabolism (S13 worksheet 5).


https://doi.org/10.1101/2020.02.17.953216


18

3.9.2 Shoots

There were 17 WGCNA modules for shoots. Out of these four modules contained transgene

dependent salt responsive genes from shoot at 3 hours as hub genes (S13 worksheet 1). Again

the analysis was restricted to the 3hr time-point since there were no transgene dependent salt

responsive genes in shoots at 51 hours. The module genes and the respective module hub genes

that were transgene dependent salt responsive are in S13 (worksheet 8 and 9, respectively).

The tan module was highest enriched for the term cellular response to iron starvation

(S13 worksheet 10) and contained bHLH43 (POPEYE/PYE: AT3G47640). The blue module, which

also contained AtCIPK16, on the other hand was enriched for the term mRNA processing

(p value = 4.81×10-19) (S13 worksheet 10). Turquoise module was enriched for ribosome

biogenesis (p value = 2.33×10-10) while magenta was enriched for water deprivation

(p value = 2.00×10-13) (S13 worksheet 10).

4 Discussion

Plant transformation has the potential to be a fast, versatile method to improve plant traits with the

ultimate goal of increasing or stabilising crop yield under adverse environmental conditions

(Gilliham et al. 2017). It has been shown that AtCIPK16 overexpression in Arabidopsis conferred

enhanced salt tolerance (Roy et al. 2013). However, the underlying molecular mechanisms that

govern the observed traits were unknown. It is important to identify the targets which are affected

by AtCIPK16, to determine whether overexpression of AtCIPK16 is not detrimental, but only

beneficial to the plant in the long term. We attempted to reduce this disparity in knowledge using a

transcriptomic approach.

The experiment was designed to study the transcriptome differences between the transgenics and

null transgenics at two different time points that have possible early (3 hours after initial salt

application) and late (51 hours after initial salt application) responses to salinity stress. Illumina

sequencing was used to generate the transcriptomic data which were subsequently mapped and

analysed to gauge the salt tolerance responses of AtCIPK16 overexpression.

4.1 Effect of AtCIPK16 transgene in salt stress

We are now able to provide laboratory and in-silico evidence to support the assumption - AtCIPK16

may elicit its function within the root cell nucleus in the presence of salt stress and this function

includes the manipulation of one or more transcription factors (TFs) as follows: a) we previously

showed that AtCIPK16 possesses a putative nuclear localisation signal (Amarasinghe et al. 2016);

here we show that b) AtCIPK16 is localised partially to the nucleus; c) AtCIPK16 has a putative


https://doi.org/10.1101/2020.02.17.953216


19

DNA binding domain which may bind it to a DNA bound molecule; d) there is minimal gene

expression differences in control roots which increases almost 4 fold in salt stressed roots; and, e)

a large number of TFs are differentially expressed.

It is likely that a regulator is needed to release the AtCIPK16 from its auto-inhibitory status and

direct towards the targets; however, RNA-Seq experiments cannot identify the potential regulators

of AtCIPK16. Nonetheless, especially in roots it could possibly be that, these regulators are

dormant until the plant is stressed. Lee et al. (2007) has suggested the possibility of CBL1 and

CBL9 to be the interacting partners of CIPK16. More recently, the ability of other kinases, such as

GRIK kinases, to release the auto-inhibitory state of SOS2 has been established (Barajas-Lopez

et al. 2018). This implies that there could be an alternative interactome for CIPKs apart from the

well-known CBLs to release it from its’ auto-inhibitory form.

4.2 Possible Downstream Activation of AtTZF1

Among the TFs differentially expressed, we identified one CCCH zinc finger (AtTZF1) that stands

out as being the only upregulated TF in both roots and shoots at 3 hours exclusive to the transgene

dependent salt responsiveness. A previous study revealed that Arabidopsis plants overexpressing

AtTZF1 showed enhanced salinity tolerance compared to the wild type due to reduced shoot Na+

accumulation, increased chlorophyll content and increased growth (Han et al. 2014). AtCIPK16

transgenics also did show reduced Na+ accumulation and increased biomass (Roy et al. 2013). We

propose AtTZF1 as a potential downstream master regulator of AtCIPK16 mediated salt stress

tolerance and suggest knockout or knockdown lines to investigate this contention. Activation of

C3H zinc fingers by post translational phosphorylaion has been shown and suggested previously

for plants and mammals (Bogamuwa and Jang 2016; Brooks and Blackshear 2013; Maldonado-

Bonilla et al. 2014; Taylor et al. 1995). It was identified that a serine downstream of the zinc finger

can be phosphorylated and enhance the activity of the TF (Cziferszky et al. 2002). It would be

interesting to know whether AtTZF1 enhances its activity through phosphorylation, and if so, could

AtCIPK16 phosphorylate AtTZF1 as well. Furthermore, in this study it was shown that there are 14

and 10 genes from roots and shoots respectively that could be transcriptionally regulated by

AtTZF1 in presence of AtCIPK16. This is an exciting path for further investigations because

manipulation of a fine-tuned TF that can control many downstream targets is a desirable feature

for the development of stress-tolerant crops without detrimental consequences or yield penalty

(Zhou et al. 2007).


https://doi.org/10.1101/2020.02.17.953216


20

4.3 Potential Regulation through Phytohormones

It was evident from the functional categorisation that hormone metabolism related genes, mainly

those related to ethylene biosynthesis (e.g. 1-Aminocyclopropane-1-carboxylic acid synthase 6;

ACC synthase 6/ACS6) (Wang et al. 2002), jasmonic acid (JA) biosynthesis and cross talk with

ethylene (e.g. Allene Oxide Synthase/AOS, AtERF1, CEJ1 and AtMYC2) [79–82] and auxin

regulation (e.g. SAUR genes) (Ren and Gray 2015), were differentially expressed in the transgenic

salt stressed transcriptome, especially at 3 hours. It could mean that phytohormone regulation is

an important aspect of AtCIPK16 mediated salt stress tolerance. Auxin responsive factors such as

SAUR proteins are well established to be involved in cross-talk between biotic and abiotic stress

tolerance (Bouzroud et al. 2018; Ghanashyam and Jain 2009; Jain and Khurana 2009; Saini et al.

2017). There is also a possibility that while AtCIPK16 affects the transcription of these genes

downstream, the phosphorylation also could mediate their activity post-translationally. Salt stress

was shown to increase ethylene production (Cao et al. 2007; Cao et al. 2006). In turn, ethylene

biosynthesis and signalling has been shown to reduce salt sensitivity (Cela et al. 2011; Tao et al.

2015).Could it be that the downstream activity of AtCIPK16 under salt stress promotes the ethylene

biosynthesis at least partly owing to increased ACS6 gene expression? If so, higher accumulation

rates of ethylene may inhibit the negative effect of ethylene receptors on salinity caused growth

arrest. AtCIPK16 overexpression in ACS6 knockouts can firmly link the function of ACS6 to salinity

tolerant AtCIPK16 overexpressing phenotypes.

Inhibition of primary root growth and promotion of lateral root growth owing to redistributed auxin

from shoots to roots as a response to ethylene synthesis has been suggested to be important in

tolerating low salinity stress in Arabidopsis (Zhao et al. 2011) and more recently in barley (Witzel

et al. 2018). The involvement of a protein encoded by another CIPK gene (SOS2; AtCIPK24) in the

process of auxin redistribution that contributes to lateral growth development in mild salinity stress

has been reported previously (Zhao et al. 2011). Data on lateral root length and number could

provide answer to the query on whether AtCIPK16 mediated salt tolerance cause a similar

morphological change.

A zinc finger protein named ZFP5 has been shown to integrate ethylene with other phytohormones

to control root hair development in Arabidopsis (An et al. 2012). In the present study, ZFP5 is co-

expressed and a direct neighbour of AtZAT10 in the green module of roots. It is possible that

AtZAT10 may be involved in modulating the activity of ZFP5. If so, the downstream effect of

AtCIPK16 overexpression may direct the ethylene signalling cascade towards ZFP5 through

AtZAT10 which can cause morphological effects such as root hair growth. Plant root hair growth is

observed in salinity stress which enables rapid influx and efflux of ions (An et al. 2012; Gilroy and


https://doi.org/10.1101/2020.02.17.953216


21

Jones 2000). It has been suggested that root hairs show preferential expression of certain K+

channels involved in K+ uptake (Ivashikina et al. 2001). Increase of K+ increases the K+/Na+ ratio

hence provides a protective function against the toxicity of Na+ within the cytosol (Maathuis and

Amtmann 1999). This could be investigated further in transgenic lines by measurements of K+ in

the roots in control and salt stressed conditions.

4.4 Transgenics Adapt to New Conditions Faster than the Wild type

There could be several possible reasons for not seeing any DEGINT at 51 hours. Firstly, AtCIPK16

mediated salinity tolerance could have already reached homeostasis by 51 hours, while it still has

not reached homeostasis in the null transgenics; this could explain why we saw reduced number

of DEGs as an effect of salt in transgenics compared to the nulls at 51 hours, in contrast to 3 hours.

Rapid adjustment to new conditions may explain the high salinity tolerance of halophytes, such as

mangroves, and this may be an important mechanism for improved salt tolerance (Krishnamurthy

et al. 2017; Liang et al. 2012; Zhu 2001). Secondly, the rapid onset of the initial osmotic adjustments

to counteract the immediate reduction in plant growth due to salinity stress, requires instant root-

to-shoot signalling once salt has been detected at the roots (Batistič and Kudla 2010; Gilroy et al.

2014; Roy et al. 2014; Roy et al. 2011; Shabala et al. 2016). It is plausible that overexpression of

AtCIPK16 may be responsible for the prompt induction of its downstream stress tolerant pathway

that aides in the rapid adjustment to the new stressful condition.

4.5 Potential of AtCIPK16 to be an Conciliator between Abiotic and Biotic Stress

Responses

In nature, plants are exposed to various concurrent abiotic and biotic stresses. Abiotic stresses

have been shown to affect the tolerance of biotic stresses negatively as well as positively. Cross-

tolerance is a term used to define the phenomenon of abiotic stress augmenting plant pathogen

resistance (Ayres 1984). An example for this is barley, Hordeum vulgare, grown in saline water

exhibiting enhanced tolerance to the barley powdery mildew fungus (Wiese et al. 2004) while, pre-

treatment of Arabidopsis with chitin, a key component of the fungal cell wall, was shown to improve

salt tolerance (Brotman et al. 2012). Recently, the identified interaction of a chitin receptor CERK1

with the Na+ induced Ca2+ channel ANN1, was shown to function both in fungal attack and salt

stress tolerance (Espinoza et al. 2017). Seeing transgene dependent salt responsive genes

involved in putative biotic stress pathways poses the question whether the AtCIPK16 mediated

molecular mechanism also confers tolerance to biotic stresses. This is plausible due to the fact that

AtCIPK16 overexpression, leads to an increased abundance of the respective CIP kinase which

can phosphorylate multiple targets, hence could potentially activate more than one pathway or


https://doi.org/10.1101/2020.02.17.953216


22

signal transduction cascade and lead to cross-tolerance. Transgene dependent salt responsive

genes being enriched for chitin response in both roots and shoots would support our speculation

on AtCIPK16's overexpression also activating biotic stress tolerance pathways. There is previous

evidence and suggestions on the involvement of CIPKs such as CIPK11 (Xie et al. 2010), CIPK25

(Huibers et al. 2009) and CIPK26 (Drerup et al. 2013) in biotic stresses.

We found an abundance of DEGs implicated in the phenylpropanoid biosynthesis pathway in

salinity stressed roots and shoots. Phenylpropanoids are considered antimicrobial compounds that

were shown to increase resistance to viral and bacterial infections or function as signalling

molecules in biotic stress responses (Naoumkina et al. 2010). Phenylpropanoid biosynthesis,

however, can lead to lignin formation which increases the rigidity of plant cell wall and stalls the

plants development (Gall et al. 2015) thereby reducing its biomass. Plants with AtCIPK16

overexpression however had higher biomass than that wild type plants grown under salinity stress

(Roy et al. 2013). Therefore, it is still unclear whether the phenylpropanoid biosynthesis is

detrimental or favourable in this particular situation.

4.6 The Proposed Model of Salinity Response in AtCIPK16 Transgenics

We propose a molecular pathway of AtCIPK16 mediated salt stress tolerance (Figure 8). Salt stress

signals may be sensed by “sensor molecules” owing to the salt stress related changes in cytosolic

Ca2+ levels in root cells. These sensor molecules then interact with/phosphorylate AtCIPK16 to

release it from the auto-inhibitory state (Barajas-Lopez et al. 2018; Sanchez-Barrena et al. 2007).

The active form of AtCIPK16 would subsequently phosphorylate multiple downstream targets

including ACS6 which in return would induce the ethylene biosynthesis. Elevated levels of ethylene

then overrule ethylene receptor induced arrest of root growth. Furthermore, ethylene can promote

auxin redistribution to promote lateral root and root hair growth which may involve ZAT10/12 and

AtZFP5 (An et al. 2012; Ivanchenko et al. 2008; Zhao et al. 2011). A possible increase in root

surface area could result in elevated uptake of K+ thereby creating a favourable K+/Na+ ratio (Cellier

et al. 2004). Furthermore, carbohydrates such as trehalose may be synthesised in the roots,

possibly as osmoprotective molecules. AtCIPK16 may even enhance the activity of AtTZF1 through

phosphorylation which leads to regulation of multiple downstream targets of AtTZF1. Through

increasing the expression of downstream targets such as ERF104 in roots, AtTZF1 might aid more

ethylene production. Additionally, AtCIPK16 may phosphorylate and mediate the activity of AOC

which leads to reinforce biosynthesis of JA that can elicit salinity tolerance responses such as

inhibition of primary root growth, as well as potential root-shoot signalling. Salt stress signalling to

shoots could activate Fe accumulation and suppress the inhibition of photosynthetic systems which


https://doi.org/10.1101/2020.02.17.953216


23

in return may promote plant shoot growth. Auxin in shoots may also promote cell growth that

increases biomass of AtCIPK16 transgenics in salinity.

4.7 Conclusion

We now have reasons to believe that AtCIPK16 mediated salinity tolerance is achieved through

the activity of a host of TFs synchronised with the regulation of phytohormones, mainly ethylene

and jasmonic acid. Modulating TFs and phytohormone mediated responses may well be a crucial

aspect in generating salt tolerant germplasm. However, this can be an audacious task, given that

both these components are involved in all aspects of a plant’s life cycle including its response

towards environmental cues. Yet, their importance is re-established by this study. The large overlap

of putative functionality of differentially expressed gene products with biotic stress responses shows

that AtCIPK16 overexpression may have the ability to elicit tolerance to multiple abiotic and biotic

stresses which is also an important trait towards developing a field-ready salt tolerant plant.

However, the importance of AtCIPK16 as a genetic tool for engineering salt tolerance in crops such

as barley needs further investigation. These investigations should shed light on the stability of the

transgene in propagation through generations, its ability to be fine-tuned by using cell specific

promoters and thereby eliminating any negative effects of AtCIPK16 overexpression.

Acknowledgements This project was funded by the Grains Research and Development Corporation (GRDC): project

UA000145 to SJR and MG. This research was also supported by the Australian Research Council

(ARC) and GRDC funding to the Australian Centre for Plant Functional Genomics (ACPFG), and

ARC projects IH130200027 to UB and SJR, and CE1400008 to MG. We further thank ACPFG and

The University of Adelaide for providing the PhD scholarship to SLA and The University of Adelaide

for providing the Adelaide Graduate Scholarship to WH. Authors also wish to thank Mr. Ashan

Hettiarachchige for creating the final high resolution images according to the guidelines from the

journal.

Conflict of Interest Statement Authors declare no conflict of interest.

Tables and Figures Table 1 Experimental design for the current study

There were 4 biological replicates per condition per experimental group for both time points. The 3

hour samples were sequenced in two technical replicates per biological sample hence indicated as

a multiple of 2.


https://doi.org/10.1101/2020.02.17.953216


24

Genotype Time 3Hr 51Hr

Treatment Root Shoot Root Shoot

Null lines Control 4×2 4×2 4 4

75 Mm Salt treated 4×2 4×2 4 4

Transgenic lines Control 4×2 4×2 4 4

75 Mm Salt treated 4×2 4×2 4 4

Total number of samples 32 32 16 16


https://doi.org/10.1101/2020.02.17.953216


25

Table 2 The genes that are putatively regulated by transcriptional activity of AtTZF1 from roots and shoots at 3 hours

The DNA binding motif of AtTZF1 ATTTATTTATTT[T|A] (Pomeranz et al. 2011; Qu et al. 2014), was scanned on the 3,000 bp upstream from the transcription

start site (TSS) of all transgene dependent salt responsive genes from roots and shoots at 3 hours (sequences were retrieved using bulk sequence retrieval

option from TAIR portal; https://www.arabidopsis.org/tools/bulk/index.jsp and scanned through FIMO from MEME suite; http://meme-suite.org/tools/fimo).

The genes with one or more positive hits with a p value ≤ 0.01 are reported here. The gene ID and the descriptions are in the first two columns. The start

and the stop site of the identified motif are in the 3rd and 4th columns, respectively. The strand the motif is predicted on is in the 5th columns. FIMO assigned

score, p-value and FDR corrected p-value (q-value are in the 6th 7th and 8th columns, respectively. The matched motif is displayed in 9th column.

Gene ID Gene description start stop strand score p-value q-value matched sequence

Root | 3Hr

AT1G12540 basic helix-loop-helix (bHLH) DNA-binding superfamily protein;(source:Araport11)

-1465 -1453 - 21.9827 5.04E-08 0.00404 ATTTATTTATTTA

AT1G43160 RELATED TO AP2 6 (RAP2.6) -1217 -1205 - 21.9827 5.04E-08 0.00404 ATTTATTTATTTA

AT1G57990 PURINE PERMEASE 18 (PUP18) -1467 -1455 + 21.9769 1.01E-07 0.00444 ATTTATTTATTTT

AT1G63057 transmembrane protein;(source:Araport11) -1265 -1253 + 21.9827 5.04E-08 0.00404 ATTTATTTATTTA

transmembrane protein;(source:Araport11) -1261 -1249 + 21.9769 1.01E-07 0.00444 ATTTATTTATTTT

AT1G64950 CYTOCHROME P450, FAMILY 89, SUBFAMILY A, POLYPEPTIDE 5 (CYP89A5)

-56 -44 + 21.9769 1.01E-07 0.00444 ATTTATTTATTTT

AT2G04110 pseudogene of expressed protein;(source:Araport11) -653 -641 - 21.9827 5.04E-08 0.00404 ATTTATTTATTTA

AT2G38240 JASMONATE-INDUCED OXYGENASE4 (JOX4) -873 -861 + 21.9827 5.04E-08 0.00404 ATTTATTTATTTA

JASMONATE-INDUCED OXYGENASE4 (JOX4) -877 -865 + 21.9827 5.04E-08 0.00404 ATTTATTTATTTA

AT3G59480 FRUCTOKINASE 7 (FRK7) -1573 -1561 - 21.9827 5.04E-08 0.00404 ATTTATTTATTTA

FRUCTOKINASE 7 (FRK7) -1577 -1565 - 21.9769 1.01E-07 0.00444 ATTTATTTATTTT

AT4G22690 CYTOCHROME P450, FAMILY 706, SUBFAMILY A, POLYPEPTIDE 1 (CYP706A1)

-1970 -1958 + 21.9769 1.01E-07 0.00444 ATTTATTTATTTT

AT4G29780 nuclease;(source:Araport11) -1659 -1647 + 21.9827 5.04E-08 0.00404 ATTTATTTATTTA

AT4G39640 GAMMA-GLUTAMYL TRANSPEPTIDASE 1 (GGT1) -518 -506 - 21.9827 5.04E-08 0.00404 ATTTATTTATTTA

AT5G13080 WRKY DNA-BINDING PROTEIN 75 (WRKY75) -1966 -1954 + 21.9769 1.01E-07 0.00444 ATTTATTTATTTT


https://www.arabidopsis.org/tools/bulk/index.jsp


https://doi.org/10.1101/2020.02.17.953216


26

AT5G56550 OXIDATIVE STRESS 3 (OXS4) -29 -17 + 21.9769 1.01E-07 0.00444 ATTTATTTATTTT

AT5G61600 ETHYLENE RESPONSE FACTOR 104 (ERF104) -1664 -1652 + 21.9769 1.01E-07 0.00444 ATTTATTTATTTT

Shoot | 3Hr

AT1G47370 RESPONSE TO THE BACTERIAL TYPE III EFFECTOR PROTEIN HOPBA1 (RBA1)

-588 -576 - 21.9769 1.01E-07 0.00464 ATTTATTTATTTT

RESPONSE TO THE BACTERIAL TYPE III EFFECTOR PROTEIN HOPBA1 (RBA1)

-877 -865 - 21.9769 1.01E-07 0.00464 ATTTATTTATTTT

AT1G73325 Kunitz family trypsin and protease inhibitor protein;(source:Araport11) -2763 -2751 - 21.9827 5.04E-08 0.00464 ATTTATTTATTTA

AT2G07042 other_RNA;(source:Araport11) -924 -912 - 21.9769 1.01E-07 0.00464 ATTTATTTATTTT

AT2G21140 PROLINE-RICH PROTEIN 2 (PRP2) -1392 -1380 + 21.9769 1.01E-07 0.00464 ATTTATTTATTTT

AT3G16720 TOXICOS EN LEVADURA 2 (ATL2) -317 -305 + 21.9827 5.04E-08 0.00464 ATTTATTTATTTA

TOXICOS EN LEVADURA 2 (ATL2) -313 -301 + 21.9769 1.01E-07 0.00464 ATTTATTTATTTT

AT3G29000 Calcium-binding EF-hand family protein;(source:Araport11) -2278 -2266 - 21.9769 1.01E-07 0.00464 ATTTATTTATTTT

AT4G18440 L-Aspartase-like family protein;(source:Araport11) -1181 -1169 + 21.9769 1.01E-07 0.00464 ATTTATTTATTTT

AT4G20860 (ATBBE22) -336 -324 + 21.9769 1.01E-07 0.00464 ATTTATTTATTTT

AT5G03150 JACKDAW (JKD) -1717 -1705 - 21.9769 1.01E-07 0.00464 ATTTATTTATTTT

AT5G04340 ZINC FINGER OF ARABIDOPSIS THALIANA 6 (ZAT6) -868 -856 + 21.9827 5.04E-08 0.00464 ATTTATTTATTTA


https://doi.org/10.1101/2020.02.17.953216


27

Figure 1 AtCIPK16 interacts with 6 AtCBL proteins

10-fold serial dilutions (OD600=1, 10-1, 10-2, 10-3) of yeast sample harbouring a DNA-binding

domain (BD)-AtCIPK16 and either an activation domain (AD)-AtCBLs or an empty vector (AD) were

grown on selection DDO (SD/-Leu/-Trp) and QDO (SD/-Leu/-Trp/-His/-Ade) media. Both DDO and

QDO selection plates were incubated at 30 °C for 6 days to allow the yeast to grow.


https://doi.org/10.1101/2020.02.17.953216


28


https://doi.org/10.1101/2020.02.17.953216


29

Figure 2 YN::AtCIPK16 and AtCBLs::YC interactions in tobacco leaves (Nicotiana

benthamiana)

Images of tobacco leaf with co-infiltration of different pairs of YN::AtCIPK16 and

AtCBLs::YC constructs. Leaves were visualised using confocal microscopy and YFP

fluorescence was captured using the wavelengths: excitation, 514 nm; emission, 525–538

nm. (A-J) Images of tobacco leaves with co-transformed with each pair of AtCBLs::YC and

YN::AtCIPK16. The images in the first column show YFP fluorescence in leaves, the

images in the second column show the bright field of leaves and the third column is a

merged image of the YFP fluorescence and bright field of leaves. Scale bar = 50 µM.


https://doi.org/10.1101/2020.02.17.953216


30

Figure 3 Identified region of AtCIPK16 protein with DNA binding affinity

The amino acid residue numbers and the respective residues are mentioned in the first and second rows, respectively; residues in the putative region with

DNA binding ability are shown in red. The server result summary for each residue is shown below the respective amino acid. Row 3-5 show the predicted

DNA binding affinity by three independent online tools (i.e. PairCoil2; grey, DP-BIND; green and BINDN; yellow); 6th row shows the polarity prediction for the

region using pepinfo server (denoted with a P in red background); 7th and 8th rows show the solvent accessibility predicted by two independent servers

(PredictProtein and NetSurfP, respectively; denoted by E in blue background.


https://doi.org/10.1101/2020.02.17.953216


31

Figure 4 AtCIPK16 gene expression in the current study

The expression values are measured in counts per million (CPM) and displayed as log2(CPM) for

clarity (y axis). Each dot represent a sample and is coloured according to the experimental

condition. Expression values are separated by genotype (i.e. null, transgenic: on top) and tissue-

time point (i.e. root 3hr, shoot 3hr, root 51 hr and shoot 51hr: right) and treatment (i.e. control, salt:

bottom). A black solid line connects the mean expression value from the two treatments in each

group of samples. A gene is considered expressed if the mean of log expression value is above 0

for a given experimental condition.


https://doi.org/10.1101/2020.02.17.953216


32

Figure 5 Contrasts tested in the

current analysis

The experimental groups

compared by Limma (Ritchie et al,

2015) to test each contrast are

shown by blue two headed arrows.

The red dashed boxes and two

headed arrow denote the contrast

to test the interaction term. The

term that defines the differentially

expressed genes for each contrast

is mentioned below each

hypothesis in bold italics


https://doi.org/10.1101/2020.02.17.953216


33

Figure 6 Number of genes differentially

identified

The number of differentially expressed

genes (DEG) for each test shown in Figure

5 are shown for; a) transgene effect in

controls (DEGTC); b) transgene effect in salt

(DEGTS); c) salt effect in transgenics

(DEGST); d) salt effect in nulls (DEGSN) and

e) transgene dependent salt responsiveness

(DEGINT). Y axis displays the number of

DEG; x axis represent the experimental

group (3R: 3 hr root; 3S: 3 hr shoot; 51R: 51

hr root; 51S: 51 hr shoot) through Limma

analysis (Ritchie et al, 2015). Yellow

denoted the upregulated genes and blue

denoted the down regulated genes. The

total number of DEGs are shown on top of

each bar. Individual numbers for up and

down regulated genes are shown along the

y axis on yellow and blue bars


https://doi.org/10.1101/2020.02.17.953216


34

Figure 7 Comparison of the DEGs

Overlap of differentially expressed genes (DEG) in roots and shoots. The comparisons that were

tested (Figure 5) are given at the top and the genes which overlap between the two tissues for that

treatment are listed on the left side of each venn diagram. DEGTC: DEG from transgenic controls;

DEGTS: DEG from transgenics in salt; DEGTC: DEG from transgene dependent salt responsiveness


https://doi.org/10.1101/2020.02.17.953216


35

Figure 8 The proposed model for AtCIPK16 mediated salinity tolerance mechanism in

Arabidopsis

The model proposes the involvement of ethylene and JA in the AtCIPK16 overexpression mediated

salinity tolerance. Blue arrows depict currently known knowledge and red arrows depict the

proposed AtCIPK16 related pathways. If a potential method of regulation is known for the proposed

pathways they are shown next to the arrow (+p: phosphorylated for enhanced activity; +t: enhanced

by transcriptional regulation; u: unknown method of regulation). The arrow heads represent the

direction of regulation. Double pointed arrows are when the direction of regulation is uncertain.


https://doi.org/10.1101/2020.02.17.953216


36

Supplementary Material

S1: Summary of k-mer baiting step to confirm the presence of the transgene in samples

a) Construct architecture of the AtCIPK16 transgene; b) the sequence in between the 35S promoter

and the AtCIPK16 exon 1; c) 35S promoter sequence that was used to bait the 5′ UTR region of

the transgene; d) NOS terminator sequence that was used to bait the 3′ UTR region of the

transgene; e) wild type AtCIPK16 5′ UTR region; f) wild type AtCIPK16 3′ UTR region; g) baiting

results from the 3 hour time point; h) baiting results from the 51 hour time point; for g and h the

columns from left to right represent the following: column1: name of the fastq file, column2: number

of baits for the transgene 3′ UTR region, column3: number of baits for the transgene 5′ UTR region,

column4: number of baits for the transgene 5′ UTR region between exon1 and the 35S promoter,

column5: number of baits for the wild type 3′ UTR region, column6: number of baits for the

transgene 5′ UTR region, column7-9: experimental conditions of each sample. Rows of g and h are

coloured for green shades to represent the shoot samples and brown shades to represent root

samples.

S2: Percentages of mapped, multi-mapped and unmapped reads from the samples using

STAR aligner

S3: The plot to show the justification of the removal of a root and a shoot sample

a) Mapped raw counts for AtCIPK16 (AT2G25090) of the 3 hour samples, b) Normalised counts

mapped to AtCIPK16. The blue semi-transparent bars indicate the samples that were removed

based on their visibly high amount of normalised reads mapped to AtCIPK16.

S4: Differentially expressed genes (DEGs) resulted from the hypothesis testing shown in

Figure 5

Where applicable the yellow colour represents up regulated genes and blue colour represents down

regulated genes.

S5: GO enrichment analysis of the DEGs performed through PlantGSEA online web server

(http://structuralbiology.cau.edu.cn/PlantGSEA/index.php)

Where applicable the yellow colour represents GO terms enriched by up regulated genes and blue

colour represents GO terms enriched by down regulated genes.

S6: Results of Kyoto Encyclopedia of Genes and Genomes (KEGG) information mining for

the DEGs using the "Search and Color Pathway" option (http://www.kegg.jp/)

S7: Selected MapMan categories that the DEGs fall into


https://doi.org/10.1101/2020.02.17.953216


37

S8: MapMan pathway analysis of the DEGs

a-i: cell function overview; j-p: regulation overview; q-u: putative biotic stress pathways.

S9: KEGG Pathways of DEG subsets that are discussed in Mapman categories and their

associated genes identified through ATTED-II.

The pathways are auto generated through the search and colour pathway option in Kyoto

Encyclopaedia of Genes and Genomes (KEGG) server (www.genome.jp/kegg/) The input genes

for each section are the up regulated DEGs for a), c), d) and e). and all DEGs for b) and f). The

associated genes that are included in the list are retrieved through the NetworkDrawer tool with

default options (Platform is automatic, CoEx option add many genes and PPI option add a few

genes) of ATTED-II server (atted.jp/). Rectangular boxes with green colour background represent

genes in the pathway, arrows represent a molecular interaction or a relationship. The red framed

green boxes with red letters show the genes that are in the input lists, each group of DEGs used to

mine the pathways in each sub figure are mentioned below the respective sub figure a)-f), empty

circles represent chemical molecules, rectangles with rounded edges shows the ink from the

current pathway to another pathway, doubled lines represent the plasma membrane, the dashed

grey lines are shown when a direct association between two molecules are unknown.

S10: KEGG pathways enriched for the DEG subsets of selected MapMan categories and

their associated genes identified through ATTED-II

S11: Novel genes involved in the AtCIPK16 dependent salt responsiveness

The gene lists identified from both roots and shoots, their GO enrichment and functional

categorisation through DAVID online web server and the novel genes that have the ability to get

phosphorylated that are identified through NetPhos3.1b online server.

S12: Identified novel AtCIPK16 transgene dependent salt responsive genes that are also

putative MAPK substrates

S13: Summary of the WGCNA analysis of DEGs

Interesting modules were selected if one or more transgene dependent salt responsive DEGs from

the respective tissue (i.e. root or shoot) are hub genes of the said module. The GO enrichment was

performed for each selected module through DAVID online tool (https://david.ncifcrf.gov/). The

pathway analysis was cone using search and Color pathway option in Kyoto Encyclopedia of Genes

and Genomes (KEGG) (www.genome.jp/kegg/).


https://david.ncifcrf.gov/

http://www.genome.jp/kegg/

https://doi.org/10.1101/2020.02.17.953216


38

References Amarasinghe S, Watson-Haigh NS, Gilliham M, Roy S, Baumann U. The evolutionary origin of CIPK16: A gene involved in enhanced salt tolerance. Mol. Phylogenet. Evol. 2016 Jul;100:135–47.

An L, Zhou Z, Sun L, Yan A, Xi W, Yu N, et al. A zinc finger protein gene ZFP5 integrates phytohormone signaling to control root hair development in Arabidopsis. Plant J. 2012 Nov 1;72(3):474–90.

Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010 Oct 27;11(10):R106.

Andrews S. FastQC: a quality control tool for high throughput sequence data. [Internet]. 2010 [cited 2015 Jan 7]. Available from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Aoki Y, Okamura Y, Tadaka S, Kinoshita K, Obayashi T. ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression. Plant Cell Physiol. 2016 Jan;57(1):e5.

Arteca RN, Arteca JM. A novel method for growing Arabidopsis thaliana plants hydroponically. Physiol. Plant. 2000 Feb 1;108(2):188–93.

Asif MA, Pearson AS, Schilling RK, Roy SJ. Opportunities for Developing Salt-tolerant Wheat and Barley Varieties. Annu. Plant Rev. Online [Internet]. American Cancer Society; 2019 [cited 2019 Oct 17]. p. 1–61. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119312994.apr0681

Ayres PG. The Interaction Between Environmental Stress Injury and Biotic Disease Physiology. Annu. Rev. Phytopathol. 1984 Sep 1;22(1):53–75.

Barajas-Lopez J de D, Moreno JR, Gamez-Arjona FM, Pardo JM, Punkkinen M, Zhu J-K, et al. Upstream kinases of plant SnRKs are involved in salt stress tolerance. Plant J. 2018 Jan 1;93(1):107–18.

Bassil E, Coku A, Blumwald E. Cellular ion homeostasis: emerging roles of intracellular NHX Na+/H+ antiporters in plant growth and development. J. Exp. Bot. 2012 Oct 1;63(16):5727–40.

Batistič O, Kudla J. Plant calcineurin B-like proteins and their interacting protein kinases. Biochim. Biophys. Acta BBA - Mol. Cell Res. 2009 Jun 1;1793(6):985–92.

Batistič O, Kudla J. Calcium: Not Just Another Ion. In: Hell R, Mendel R-R, editors. Cell Biol. Met. Nutr. [Internet]. Springer Berlin Heidelberg; 2010 [cited 2016 Oct 18]. p. 17–54. Available from: http://link.springer.com/chapter/10.1007/978-3-642-10613-2_2

Baxter A, Mittler R, Suzuki N. ROS as key players in plant stress signalling. J. Exp. Bot. 2014 Mar 1;65(5):1229–40.

Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B Methodol. 1995;57(1):289–300.

Blom N, Gammeltoft S, Brunak S. Sequence and structure-based prediction of eukaryotic protein phosphorylation sites1 1Edited by F. E. Cohen. J. Mol. Biol. 1999 Dec 17;294(5):1351–62.


https://doi.org/10.1101/2020.02.17.953216


39

Bogamuwa S, Jang J-C. Plant Tandem CCCH Zinc Finger Proteins Interact with ABA, Drought, and Stress Response Regulators in Processing-Bodies and Stress Granules. PLOS ONE. 2016 Mar 15;11(3):e0151574.

Bouzroud S, Gouiaa S, Hu N, Bernadac A, Mila I, Bendaou N, et al. Auxin Response Factors (ARFs) are potential mediators of auxin action in tomato response to biotic and abiotic stress (Solanum lycopersicum). PLOS ONE. 2018 Feb 28;13(2):e0193517.

Brooks SA, Blackshear PJ. Tristetraprolin (TTP): Interactions with mRNA and proteins, and current thoughts on mechanisms of action. Biochim. Biophys. Acta. 2013;1829(0):666–79.

Brotman Y, Landau U, Pnini S, Lisec J, Balazadeh S, Mueller-Roeber B, et al. The LysM Receptor-Like Kinase LysM RLK1 Is Required to Activate Defense and Abiotic-Stress Responses Induced by Overexpression of Fungal Chitinases in Arabidopsis Plants. Mol. Plant. 2012 Sep 1;5(5):1113–24.

Cao W-H, Liu J, He X-J, Mu R-L, Zhou H-L, Chen S-Y, et al. Modulation of Ethylene Responses Affects Plant Salt-Stress Responses. Plant Physiol. 2007 Feb 1;143(2):707–19.

Cao W-H, Liu J, Zhou Q-Y, Cao Y-R, Zheng S-F, Du B-X, et al. Expression of tobacco ethylene receptor NTHK1 alters plant responses to salt stress. Plant Cell Environ. 2006 Jul 1;29(7):1210–9.

Cela J, Chang C, Munné-Bosch S. Accumulation of γ- Rather than α-Tocopherol Alters Ethylene Signaling Gene Expression in the vte4 Mutant of Arabidopsis thaliana. Plant Cell Physiol. 2011 Aug 1;52(8):1389–400.

Cellier F, Conéjéro G, Ricaud L, Luu DT, Lepetit M, Gosti F, et al. Characterization of AtCHX17, a member of the cation/H+ exchangers, CHX family, from Arabidopsis thaliana suggests a role in K+ homeostasis. Plant J. 2004 Sep 1;39(6):834–46.

Chen Z, Pottosin II, Cuin TA, Fuglsang AT, Tester M, Jha D, et al. Root Plasma Membrane Transporters Controlling K+/Na+ Homeostasis in Salt-Stressed Barley. Plant Physiol. 2007 Dec 1;145(4):1714–25.

Cheng M-C, Liao P-M, Kuo W-W, Lin T-P. The Arabidopsis ETHYLENE RESPONSE FACTOR1 Regulates Abiotic Stress-Responsive Gene Expression by Binding to Different cis-Acting Elements in Response to Different Stress Signals. PLANT Physiol. 2013 Jul 1;162(3):1566–82.

Cziferszky A, Mach RL, Kubicek CP. Phosphorylation Positively Regulates DNA Binding of the Carbon Catabolite Repressor Cre1 of Hypocrea jecorina(Trichoderma reesei). J. Biol. Chem. 2002 Apr 26;277(17):14688–94.

D’Angelo C, Weinl S, Batistic O, Pandey GK, Cheong YH, Schültke S, et al. Alternative complex formation of the Ca2+-regulated protein kinase CIPK1 controls abscisic acid-dependent and independent stress responses in Arabidopsis. Plant J. 2006 Dec 1;48(6):857–72.

Deinlein U, Stephan AB, Horie T, Luo W, Xu G, Schroeder JI. Plant salt-tolerance mechanisms. Trends Plant Sci. 2014 Jun;19(6):371–9.

Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003 Apr 3;4(5):P3.

Diédhiou CJ, Golldack D. Salt-dependent regulation of chloride channel transcripts in rice. Plant Sci. 2006 Apr 1;170(4):793–800.


https://doi.org/10.1101/2020.02.17.953216


40

Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinforma. Oxf. Engl. 2013 Jan 1;29(1):15–21.

Fahad S, Hussain S, Matloob A, Khan FA, Khaliq A, Saud S, et al. Phytohormones and plant responses to salinity stress: a review. Plant Growth Regul. 2015 Mar 1;75(2):391–404.

FAOSTAT. Food and Agriculture Organization fo the United Nations [Internet]. Food Agric. Organ. Fo U. N. 2014 [cited 2014 Apr 22]. Available from: http://www.fao.org/economic/ess/en/#.U1XSWvmSymw

Gall HL, Philippe F, Domon J-M, Gillet F, Pelloux J, Rayon C. Cell Wall Metabolism in Response to Abiotic Stress. Plants. 2015 Feb 16;4(1):112–66.

Ghanashyam C, Jain M. Role of auxin-responsive genes in biotic stress responses. Plant Signal. Behav. 2009 Sep;4(9):846–8.

Gilliham M, Able JA, Roy SJ. Translating knowledge about abiotic stress tolerance to breeding programmes. Plant J. 2017 Feb 1;n/a-n/a.

Gilroy S, Jones DL. Through form to function: root hair development and nutrient uptake. Trends Plant Sci. 2000 Feb 1;5(2):56–60.

Gilroy S, Suzuki N, Miller G, Choi W-G, Toyota M, Devireddy AR, et al. A tidal wave of signals: calcium and ROS at the forefront of rapid systemic signaling. Trends Plant Sci. 2014 Oct;19(10):623–30.

Grabov A. Plant KT/KUP/HAK Potassium Transporters: Single Family – Multiple Functions. Ann. Bot. 2007 Jun 1;99(6):1035–41.

Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics. 2011 Apr 1;27(7):1017–8.

Hamamoto S, Horie T, Hauser F, Deinlein U, Schroeder JI, Uozumi N. HKT transporters mediate salt stress resistance in plants: from structure and function to the field. Curr. Opin. Biotechnol. 2015 Apr;32:113–20.

Han G, Wang M, Yuan F, Sui N, Song J, Wang B. The CCCH zinc finger protein gene AtZFP1 improves salt resistance in Arabidopsis thaliana. Plant Mol. Biol. 2014 Oct;86(3):237–53.

Hanin M, Ebel C, Ngom M, Laplaze L, Masmoudi K. New Insights on Plant Salt Tolerance Mechanisms and Their Potential Use for Breeding. Front. Plant Sci. [Internet]. 2016 Nov 29 [cited 2017 Apr 4];7. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5126725/

Hashimoto K, Eckert C, Anschutz U, Scholz M, Held K, Waadt R, et al. Phosphorylation of Calcineurin B-like (CBL) Calcium Sensor Proteins by Their CBL-interacting Protein Kinases (CIPKs) Is Required for Full Activity of CBL-CIPK Complexes toward Their Target Proteins. J. Biol. Chem. 2012 Mar 9;287(11):7956–68.

Huibers RP, de Jong M, Dekter RW, Van den Ackerveken G. Disease-Specific Expression of Host Genes During Downy Mildew Infection of Arabidopsis. Mol. Plant. Microbe Interact. 2009 Aug 5;22(9):1104–15.

Hwang S, Gou Z, Kuznetsov IB. DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins. Bioinforma. Oxf. Engl. 2007 Mar 1;23(5):634–6.


https://doi.org/10.1101/2020.02.17.953216


41

Ivanchenko MG, Muday GK, Dubrovsky JG. Ethylene–auxin interactions regulate lateral root initiation and emergence in Arabidopsis thaliana. Plant J. 2008 Jul 1;55(2):335–47.

Ivashikina N, Becker D, Ache P, Meyerhoff O, Felle HH, Hedrich R. K+ channel profile and electrical properties of Arabidopsis root hairs. FEBS Lett. 2001 Nov 23;508(3):463–9.

Jain M, Khurana JP. Transcript profiling reveals diverse roles of auxin-responsive genes during reproductive development and abiotic stress in rice. FEBS J. 2009 Jun 1;276(11):3148–62.

James RA, Blake C, Byrt CS, Munns R. Major genes for Na+ exclusion, Nax1 and Nax2 (wheat HKT1;4 and HKT1;5), decrease Na+ accumulation in bread wheat leaves under saline and waterlogged conditions. J. Exp. Bot. 2011 May 1;62(8):2939–47.

Jha D, Shirley N, Tester M, Roy SJ. Variation in salinity tolerance and shoot sodium accumulation in Arabidopsis ecotypes linked to differences in the natural expression levels of transporters involved in sodium transport. Plant Cell Environ. 2010 May 1;33(5):793–804.

Ji H, Pardo JM, Batelli G, Oosten MJV, Bressan RA, Li X. The Salt Overly Sensitive (SOS) Pathway: Established and Emerging Roles. Mol. Plant. 2013 Jan 25;sst017.

Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017 Jan 4;45(D1):D353–61.

Kim K-N. CIPK3, a Calcium Sensor-Associated Protein Kinase That Regulates Abscisic Acid and Cold Signal Transduction in Arabidopsis. PLANT CELL ONLINE. 2003 Feb 1;15(2):411–23.

Krishnamurthy P, Mohanty B, Wijaya E, Lee D-Y, Lim T-M, Lin Q, et al. Transcriptomics analysis of salt stress tolerance in the roots of the mangrove Avicennia officinalis. Sci. Rep. 2017 Aug 30;7(1):10031.

Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Dec 29;9(1):559.

Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29.

Li W, A C, M U, T G, H M, S S, et al. The EMBL-EBI bioinformatics web and programmatic tools framework. Nucleic Acids Res. 2015 2015;43(W1):W580-4.

Liang S, Fang L, Zhou R, Tang T, Deng S, Dong S, et al. Transcriptional Homeostasis of a Mangrove Species, Ceriops tagal, in Saline Environments, as Revealed by Microarray Analysis. PLOS ONE. 2012 May 4;7(5):e36499.

Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinforma. Oxf. Engl. 2014 Apr 1;30(7):923–30.

Liu J, Ishitani M, Halfter U, Kim C-S, Zhu J-K. The Arabidopsis thaliana SOS2 gene encodes a protein kinase that is required for salt tolerance. Proc. Natl. Acad. Sci. U. S. A. 2000 Mar 28;97(7):3730–4.

Luan S. The CBL–CIPK network in plant calcium signaling. Trends Plant Sci. 2009 Jan;14(1):37–42.


https://doi.org/10.1101/2020.02.17.953216


42

Maathuis FJ, Amtmann A. K+ nutrition and Na+ toxicity: the basis of cellular K+/Na+ ratios. Ann. Bot. 1999;84(2):123–133.

Maldonado-Bonilla LD, Eschen-Lippold L, Gago-Zachert S, Tabassum N, Bauer N, Scheel D, et al. The Arabidopsis Tandem Zinc Finger 9 Protein Binds RNA and Mediates Pathogen-Associated Molecular Pattern-Triggered Immune Responses. Plant Cell Physiol. 2014 Feb 1;55(2):412–25.

Mao J, Manik SMN, Shi S, Chao J, Jin Y, Wang Q, et al. Mechanisms and Physiological Roles of the CBL-CIPK Networking System in Arabidopsis thaliana. Genes. 2016 Sep 8;7(9):62.

McDonnell AV, Jiang T, Keating AE, Berger B. Paircoil2: improved prediction of coiled coils from sequence. Bioinformatics. 2006 Feb 1;22(3):356–8.

Meng X, Xu J, He Y, Yang K-Y, Mordorski B, Liu Y, et al. Phosphorylation of an ERF Transcription Factor by Arabidopsis MPK3/MPK6 Regulates Plant Defense Gene Induction and Fungal Resistance. Plant Cell Online. 2013 Mar 1;25(3):1126–42.

Mian A, Oomen RJFJ, Isayenkov S, Sentenac H, Maathuis FJM, Véry A-A. Over-expression of an Na+- and K+-permeable HKT transporter in barley improves salt tolerance. Plant J. 2011 Nov 1;68(3):468–79.

Mittler R, Vanderauwera S, Suzuki N, Miller G, Tognetti VB, Vandepoele K, et al. ROS signaling: the new wave? Trends Plant Sci. 2011 Jun;16(6):300–9.

Munns R, Gilliham M. Salinity tolerance of crops – what is the cost? New Phytol. 2015 Nov 1;208(3):668–73.

Munns R, Tester M. Mechanisms of Salinity Tolerance. Annu. Rev. Plant Biol. 2008 Jun;59(1):651–81.

Naoumkina MA, Zhao Q, Gallego-Giraldo L, Dai X, Zhao PX, Dixon RA. Genome-wide analysis of phenylpropanoid defence pathways. Mol. Plant Pathol. 2010 Nov 1;11(6):829–46.

Nguyen XC, Kim SH, Lee K, Kim KE, Liu X-M, Han HJ, et al. Identification of a C2H2-type zinc finger transcription factor (ZAT10) from Arabidopsis as a substrate of MAP kinase. Plant Cell Rep. 2012 Apr 1;31(4):737–45.

Park J-H, Halitschke R, Kim HB, Baldwin IT, Feldmann KA, Feyereisen R. A knock-out mutation in allene oxide synthase results in male sterility and defective wound signal transduction in Arabidopsis due to a block in jasmonic acid biosynthesis. Plant J. 2002 Jul 1;31(1):1–12.

Peleg Z, Blumwald E. Hormone balance and abiotic stress tolerance in crop plants. Curr. Opin. Plant Biol. 2011 Jun 1;14(3):290–5.

Petersen B, Petersen TN, Andersen P, Nielsen M, Lundegaard C. A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct. Biol. 2009 Jul 31;9(1):51.

Pomeranz M, Zhang L, Finer J, Jang J-C. Can AtTZF1 act as a transcriptional activator or repressor in plants? Plant Signal. Behav. 2011 May;6(5):719–22.

Popescu SC, Popescu GV, Bachan S, Zhang Z, Gerstein M, Snyder M, et al. MAPK target networks in Arabidopsis thaliana revealed using functional protein microarrays. Genes Dev. 2009 Jan 1;23(1):80–92.


https://doi.org/10.1101/2020.02.17.953216


43

Qiu J, Henderson SW, Tester M, Roy SJ, Gilliham M. SLAH1, a homologue of the slow type anion channel SLAC1, modulates shoot Cl − accumulation and salt tolerance in Arabidopsis thaliana. J. Exp. Bot. 2016 Aug 1;67(15):4495–505.

Qu J, Kang SG, Wang W, Musier-Forsyth K, Jang J-C. Arabidopsis thaliana tandem zinc finger 1 (AtTZF1) protein in RNA binding and decay. Plant J. Cell Mol. Biol. 2014 May;78(3):452–67.

Ren H, Gray WM. SAUR Proteins as Effectors of Hormonal and Environmental Signals in Plant Growth. Mol. Plant. 2015 Aug 3;8(8):1153–64.

Rengasamy P. World salinization with emphasis on Australia. J. Exp. Bot. 2006 Mar 1;57(5):1017–23.

Rengasamy P. Soil processes affecting crop production in salt-affected soils. Funct. Plant Biol. 2010 Jul 23;37(7):613–20.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 Jan 20;gkv007.

Rosa M, Prado C, Podazza G, Interdonato R, González JA, Hilal M, et al. Soluble sugars—Metabolism, sensing and abiotic stress. Plant Signal. Behav. 2009 May;4(5):388–93.

Rost B, Yachdav G, Liu J. The PredictProtein server. Nucleic Acids Res. 2004 Jul 1;32(suppl 2):W321–6.

Roy SJ, Huang W, Wang XJ, Evrard A, Schmöckel SM, Zafar ZU, et al. A novel protein kinase involved in Na+ exclusion revealed from positional cloning. Plant Cell Environ. 2013 Mar 1;36(3):553–68.

Roy SJ, Negrão S, Tester M. Salt resistant crop plants. Curr. Opin. Biotechnol. 2014 Apr;26:115–24.

Roy SJ, Tucker EJ, Tester M. Genetic analysis of abiotic stress tolerance in crops. Curr. Opin. Plant Biol. 2011 Jun;14(3):232–9.

Ryu H, Cho Y-G. Plant hormones in salt stress tolerance. J. Plant Biol. 2015 Jun;58(3):147–55.

Saini S, Sharma I, Pati PK. Integrating the Knowledge of Auxin Homeostasis with Stress Tolerance in Plants. Mech. Plant Horm. Signal. Stress [Internet]. Wiley-Blackwell; 2017 [cited 2018 Aug 14]. p. 53–70. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118889022.ch3

Sanchez-Barrena MJ, Fujii H, Angulo I, Martinez-Ripoll M, Zhu J-K, Albert A. The Structure of the C-Terminal Domain of the Protein Kinase AtSOS2 Bound to the Calcium Sensor AtSOS3. Mol. Cell. 2007 May 11;26(3):427–35.

Schachtman DP, Schroeder JI, Lucas WJ, Anderson JA, Gaber RF. Expression of an inward-rectifying potassium channel by the Arabidopsis KAT1 cDNA. Science. 1992 Dec 4;258(5088):1654–8.

Schroeder JI, Delhaize E, Frommer WB, Guerinot ML, Harrison MJ, Herrera-Estrella L, et al. Using membrane transporters to improve crops for sustainable food production. Nature. 2013 May 2;497(7447):60–6.


https://doi.org/10.1101/2020.02.17.953216


44

Shabala S. Learning from halophytes: physiological basis and strategies to improve abiotic stress tolerance in crops. Ann. Bot. 2013 Oct 1;mct205.

Shabala S, White RG, Djordjevic MA, Ruan Y-L, Mathesius U. Root-to-shoot signalling: integration of diverse molecules, pathways and functions. Funct. Plant Biol. 2016;43(2):87.

Shabala S, Wu H, Bose J. Salt stress sensing and early signalling events in plant roots: Current knowledge and hypothesis. Plant Sci. 2015 Dec;241:109–19.

Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 2004;3:Article3.

Tao J-J, Chen H-W, Ma B, Zhang W-K, Chen S-Y, Zhang J-S. The Role of Ethylene in Plants Under Salinity Stress. Plant Nutr. 2015;1059.

Taylor GA, Thompson MJ, Lai WS, Blackshear PJ. Phosphorylation of Tristetraprolin, a Potential Zinc Finger Transcription Factor, by Mitogen Stimulation in Intact Cells and by Mitogen-activated Protein Kinase in Vitro. J. Biol. Chem. 1995 Jun 2;270(22):13341–7.

Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, et al. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. Cell Mol. Biol. 2004 Mar;37(6):914–39.

Thoday-Kennedy EL, Jacobs AK, Roy SJ. The role of the CBL–CIPK calcium signalling network in regulating ion transport in response to abiotic stress. Plant Growth Regul. 2015 May;76(1):3–12.

Tripathi V, Parasuraman B, Laxmi A, Chattopadhyay D. CIPK6, a CBL-interacting protein kinase is required for development and salt tolerance in plants. Plant J. 2009 Jun 1;58(5):778–90.

Vogel MO, Gomez-Perez D, Probst N, Dietz K-J. Combinatorial Signal Integration by APETALA2/Ethylene Response Factor (ERF)-Transcription Factors and the Involvement of AP2-2 in Starvation Response. Int. J. Mol. Sci. 2012 May 16;13(5):5933–51.

Wang KL-C, Li H, Ecker JR. Ethylene Biosynthesis and Signaling Networks. Plant Cell. 2002 May 1;14(suppl 1):S131–51.

Wang L, Brown SJ. BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W243-248.

Wang S, Su SZ, Wu Y, Li SP, Shan XH, Liu HK, et al. Overexpression of maize chloride channel gene <Emphasis Type="Italic">ZmCLC-d</Emphasis> in <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis> improved its stress resistance. Biol. Plant. 2015 Jan 1;59(1):55–64.

Wasternack C, Hause B. Jasmonates: biosynthesis, perception, signal transduction and action in plant stress response, growth and development. An update to the 2007 review in Annals of Botany. Ann. Bot. 2013 Jun 1;111(6):1021–58.

Wiese J, Kranz T, Schubert S. Induction of Pathogen Resistance in Barley by Abiotic Stress. Plant Biol. 2004 Sep 1;6(5):529–36.

Witzel K, Matros A, Møller ALB, Ramireddy E, Finnie C, Peukert M, et al. Plasma membrane proteome analysis identifies a role of barley Membrane Steroid Binding Protein in root architecture response to salinity. Plant Cell Environ. 2018;pre-print.


https://doi.org/10.1101/2020.02.17.953216


45

Wungrampha S, Joshi R, Singla-Pareek SL, Pareek A. Photosynthesis and salinity: are these mutually exclusive? Photosynthetica. 2018 Mar 1;56(1):366–81.

Yi X, Du Z, Su Z. PlantGSEA: a gene set enrichment analysis toolkit for plant community. Nucleic Acids Res. 2013 Jul;41(Web Server issue):W98-103.

Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. [Internet]. 2005 [cited 2014 Jun 18];4(1). Available from: http://www.degruyter.com/view/j/sagmb.2005.4.1/sagmb.2005.4.1.1128/sagmb.2005.4.1.1128.xml

Zhao Y, Dong W, Zhang N, Ai X, Wang M, Huang Z, et al. A Wheat Allene Oxide Cyclase Gene Enhances Salinity Tolerance via Jasmonate Signaling1[C][W]. Plant Physiol. 2014 Feb;164(2):1068–76.

Zhao Y, Wang T, Zhang W, Li X. SOS3 mediates lateral root development under low salt stress through regulation of auxin redistribution and maxima in Arabidopsis. New Phytol. 2011 Mar 1;189(4):1122–34.

Zhou F, Sosa J, Feldmann KA. High throughput approaches for the identification of salt tolerance genes in plants. Adv. Mol. Breed. Drought Salt Toler. Crops [Internet]. 1st ed. Springer Netherlands; 2007 [cited 2018 Feb 6]. p. 359–79. Available from: https://arizona.pure.elsevier.com/en/publications/high-throughput-approaches-for-the-identification-of-salt-toleran

Zhu J-K. Plant salt tolerance. Trends Plant Sci. 2001 Feb 1;6(2):66–71.


https://doi.org/10.1101/2020.02.17.953216


Documents

AtCIPK16 Mediates Salt Stress Potentially Through ... › content › 10.1101 › 2020.02.17.953216v2.ful… · AtCIPK16 Mediates Salt Stress Potentially Through Phytohormones and